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Elasticsearch offers three main autocomplete strategies, each with unique benefits and trade-offs.

Elasticsearch fuzzy search allows finding results even with typos in queries.

Stemming and normalization help search engines match various word forms for better results.

Elasticsearch offers two pagination methods: from & size for shallow pages and search_after for deep pages.

Elasticsearch uses analyzers and synonyms to improve search accuracy by connecting similar words.

Optimizing Elasticsearch involves using filters, caching, and measuring performance.

Tokenization and analyzers help search engines process and match text effectively.

Deep pagination in Elasticsearch can slow down searches significantly, but there are efficient methods to handle it.

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

Elasticsearch ranks search results using BM25 based on relevance scoring.